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Breast cancer screening is currently predominantly based on mammography, tainted with the occurrence of both false positivity and false negativity, urging for innovative strategies, as effective detection of early-stage breast cancer bears the potential to reduce mortality. | Kepesidis et al. BMC Cancer 2021 21 1287 https doi.org 10.1186 s12885-021-09017-7 RESEARCH Open Access Breast-cancer detection using blood-based infrared molecular fingerprints Kosmas V. Kepesidis1 2 Masa Bozic Iven1 Marinus Huber1 2 Nashwa Abdel Aziz3 Sharif Kullab3 Ahmed Abdelwarith3 Abdulrahman Al Diab3 Mohammed Al Ghamdi3 Muath Abu Hilal3 Mohun R. K. Bahadoor4 Abhishake Sharma4 Farida Dabouz4 Maria Arafah5 Abdallah M. Azzeer6 Ferenc Krausz1 2 Khalid Alsaleh3 Mihaela Zigman1 2 and Jean Marc Nabholtz1 3 Abstract Background Breast cancer screening is currently predominantly based on mammography tainted with the occur rence of both false positivity and false negativity urging for innovative strategies as effective detection of early-stage breast cancer bears the potential to reduce mortality. Here we report the results of a prospective pilot study on breast cancer detection using blood plasma analyzed by Fourier-transform infrared FTIR spectroscopy a rapid cost-effec tive technique with minimal sample volume requirements and potential to aid biomedical diagnostics. FTIR has the capacity to probe health phenotypes via the investigation of the full repertoire of molecular species within a sample at once within a single measurement in a high-throughput manner. In this study we take advantage of cross-molecu lar fingerprinting to probe for breast cancer detection. Methods We compare two groups 26 patients diagnosed with breast cancer to a same-sized group of age-matched healthy asymptomatic female participants. Training with support-vector machines SVM we derive classification models that we test in a repeated 10-fold cross-validation over 10 times. In addition we investigate spectral informa tion responsible for BC identification using statistical significance testing. Results Our models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve AUC of the receiver operating characteristic ROC . In addition we uncover a .
